This engineering role focuses on building and evolving the shared data infrastructure for NVIDIA's Digital Marketing Operations team. The Senior Data Engineer will power customer engagement, campaign intelligence, and marketing analytics at scale by transforming customer behavioral signals, audience definitions, consent records, campaign metadata, and activation outputs into trusted, governed data products.
Responsibilities
Own a key data domain within Digital Marketing Operations, spanning pipelines, data products, audience services, and consumer teams
Shape the architecture and interfaces while anticipating needs related to scale, reliability, performance, privacy compliance, security, cost, and compatibility
Lead complex, cross-functional initiatives
Collaborate across engineering, marketing, and operations stakeholders to translate requirements into clear data architectures
Write high-quality code in the critical path
Drive integrations safely into production
Architect, implement, and evolve batch and streaming pipelines that ingest, transform, reconcile, and serve customer behavioral, audience, campaign, consent, and activation data at scale
Build reliable, maintainable systems leveraging distributed processing frameworks, workflow orchestration tools, cloud data warehouses, object storage, and pub/sub messaging platforms
Build and evolve shared data quality frameworks and engineering standards that improve pipeline reliability, consistency, and efficiency across teams
Lead production incident investigations across pipelines, distributed processing, storage, and cloud services
Coordinate across system owners to establish root cause, drive resolution, and implement preventive improvements
Embed privacy and compliance by design across the data platform
Implement and enforce data governance, consent propagation, GDPR and PIPL compliance controls, data minimization, access boundaries, right-to-erasure workflows, and auditable data lineage as first-class engineering requirements
Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams
Deliver trusted data assets that make customer and campaign data widely accessible to marketing consumers without sacrificing accuracy, privacy, or maintainability
Provide technical leadership through architecture and data model reviews, hands-on mentorship of junior engineers, and evidence-based resolution of difficult tradeoffs
Raise engineering quality across teams through reusable patterns, clear decisions, and sustained follow-through
Requirements
8+ years of relevant industry experience with a Bachelor's or equivalent experience, and a Master's degree or equivalent in Computer Science, Engineering, or a related field
Millions of jobs, with real people getting hired every day
200,000+
New jobs added daily
7,000,000+
Verified job listings
1,000,000+
Tailored applications submitted
A sustained record of designing, implementing, and operating production data pipelines, data platforms, or distributed data systems
End-to-end technical ownership of a multi-pipeline domain or a complex cross-team data engineering initiative
Proficiency in Python and one or more modern programming languages such as Java or Golang
Strong SQL fundamentals and the ability to write clean, testable, production-quality code
Deep hands-on experience with distributed data processing frameworks such as Apache Spark, and cloud-managed ETL services
Experience building and operating production pipelines with workflow orchestration tools such as Airflow or equivalent
Strong experience with cloud data platform services, including object storage, cloud data warehouses, managed transformation services, and related infrastructure
Demonstrated ability to architect and operate large-scale data workflows in a cloud environment
Experience with data lake and lakehouse architectures, including open table formats such as Apache Iceberg
Ability to design, manage, and optimize large-scale data storage for reliable analytical consumption
Strong SQL and NoSQL skills and data-modeling proficiency, including practical depth in query execution, incremental processing, schema evolution, consistency, analytical consumption patterns, idempotency, replay, late-arriving data, partial failure, and cross-system correctness
Experience building systems that move customer, behavioral, consent, campaign, or audience data across multiple platforms, with strong attention to data quality, governance, and operational reliability
Demonstrated skill in diagnosing production failures across distributed systems by analyzing logs, metrics, traces, query plans, and profiles, followed by implementing and validating long-lasting resolutions
Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, privacy, compatibility, and maintainability
Experience guiding major migrations or architectural changes across teams without interrupting production service
Nice to have:
Experience with MarTech stacks and B2C customer data at scale, including CDP integrations, reverse ETL, identity resolution, customer 360 or profile services, audience activation pipelines, campaign metadata, tracking code management, and multi-touch attribution
Experience designing and operating high-throughput, fault-tolerant streaming systems at scale, including exactly-once delivery semantics, consumer lag management, schema registry patterns, and replay strategies for large data volumes
Benefits
Equity and benefits
Important Details
Application deadline: October 23, 2026
About the Company
NVIDIA is a technology company that uses AI tools in its recruiting processes.
Since its founding in 1993, NVIDIA has been a pioneer in accelerated computing.
